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doi:10.1590/s1413-77042012000200012

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“…We use SDO Atmospheric Imaging Assembly (AIA; Lemen et al 2012) observations to prepare our EUV Carrington maps from which coronal holes can be detected. We downloaded 180 s worth of data for the 304 Å, 171 Å, 193 Å, and 211 Å filters each day of the Carrington rotations relevant to PSP encounters 1-3.…”

Section: Euv Coronal Hole Synoptic Mapsmentioning

confidence: 99%

Constraining Global Coronal Models with Multiple Independent Observables

Badman

Brooks

Poirier

et al. 2022

ApJ

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Global coronal models seek to produce an accurate physical representation of the Sun’s atmosphere that can be used, for example, to drive space-weather models. Assessing their accuracy is a complex task, and there are multiple observational pathways to provide constraints and tune model parameters. Here, we combine several such independent constraints, defining a model-agnostic framework for standardized comparison. We require models to predict the distribution of coronal holes at the photosphere, and neutral line topology at the model’s outer boundary. We compare these predictions to extreme-ultraviolet (EUV) observations of coronal hole locations, white-light Carrington maps of the streamer belt, and the magnetic sector structure measured in situ by Parker Solar Probe and 1 au spacecraft. We study these metrics for potential field source surface (PFSS) models as a function of source surface height and magnetogram choice, as well as comparing to the more physical Wang–Sheeley–Arge (WSA) and the Magnetohydrodynamic Algorithm outside a Sphere (MAS) models. We find that simultaneous optimization of PFSS models to all three metrics is not currently possible, implying a trade-off between the quality of representation of coronal holes and streamer belt topology. WSA and MAS results show the additional physics that they include address this by flattening the streamer belt while maintaining coronal hole sizes, with MAS also improving coronal hole representation relative to WSA. We conclude that this framework is highly useful for inter- and intra-model comparisons. Integral to the framework is the standardization of observables required of each model, evaluating different model aspects.

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Section: Euv Coronal Hole Synoptic Mapsmentioning

confidence: 99%